Abstract: A method of tracking a cell through a plurality of images includes selecting the cell in at least one image obtained at a first time, generating a track of the cell through a plurality of images, including the at least one image, obtained at different times using a backward tracking, and generating a cell tree lineage of the cell using the track.

Abstract: An AMD prediction model utilizes an OCT image estimation model. The OCT image estimation module is created by segmenting an OCT image to generate an OCT projection image for each of multiple biological layers; extracting from each of the generated OCT projection images a first set of features; extracting a second set of features from an input retinal fundus image; for each respective biological layer, registering the input retinal fundus image to the respective OCT projection image by matching at least some of the second set of features with corresponding ones of the first set of features; repeating the above with changes to the input retinal fundus image; and modelling how the changes to the input retinal fundus image are manifest at the correspondingly registered projection images. Estimated OCT projection images can then be generated for the multiple biological layers from a given retinal fundus image.

Abstract: An embodiment of the invention receives by an interface a retinal image from a patient, and identifies by a feature extraction device vessel fragments in the retinal image. The vessel fragments include at least a portion of a major vessel and at least a portion of a branch connected to a major vessel. A processor computes estimated blood flow velocities in the vessel fragments with a blood flow velocity estimation model and determines actual blood flow velocities in the vessel fragments. An analysis engine compares the actual blood flow velocities in the vessel fragments to the estimated blood flow velocities in the vessel fragments. The analysis engine detects a candidate plaque affected vessel fragment when the estimated blood flow velocities in the vessel fragments differs from the actual blood flow velocities in the vessel fragments by a predetermined amount.

Abstract: A method, system, and computer program product to perform image-based estimation of a risk of a vehicle having a specified status include receiving images from one or more cameras, obtaining one or more vehicle images of the vehicle from the image, classifying the vehicle based on the one or more vehicle images to determine a vehicle classification, extracting features from the one or more vehicle images based on the vehicle classification, and comparing the features with risk indicators to determine estimation of the risk. Instructions are provided for an action based on the risk.

Abstract: A knowledge base is generated based on eye tracking, audio monitoring and image annotations, for determining image features from given images and sequences of image features to focus on in analyzing an image. An eye tracker monitors eye movements of a user analyzing an image and generates a sequence of eye movements. A user interface receives annotations on the image. Audio data received via a microphone is translated into text and keywords are extracted. The sequence of eye movements, the annotations and the keywords are correlated according to their time of occurrence. Image features are extracted from the image and mapped with the sequence of eye movements, the annotations and the keywords that are correlated. A recurrent neural network model is generated based on the mapped image features and predicts a likelihood of an expert image analyzer focusing on a feature in a given new image.

Abstract: A computer-implemented method obtains at least one image from which severity of a given pathological condition presented in the at least one image is to be classified. The method generates a hybrid image representation of the at least one obtained image. The hybrid image representation comprises a concatenation of a discriminative pathology histogram, a generative pathology histogram, and a fully connected representation of a trained baseline convolutional neural network. The hybrid image representation is used to train a classifier to classify the severity of the given pathological condition presented in the at least one image. One non-limiting example of a pathological condition whose severity can be classified with the above method is diabetic retinopathy.

Abstract: An AMD prediction model utilizes an OCT image estimation model. The OCT image estimation module is created by segmenting an OCT image to generate an OCT projection image for each of multiple biological layers; extracting from each of the generated OCT projection images a first set of features; extracting a second set of features from an input retinal fundus image; for each respective biological layer, registering the input retinal fundus image to the respective OCT projection image by matching at least some of the second set of features with corresponding ones of the first set of features; repeating the above with changes to the input retinal fundus image; and modelling how the changes to the input retinal fundus image are manifest at the correspondingly registered projection images. Estimated OCT projection images can then be generated for the multiple biological layers from a given retinal fundus image.

Abstract: A dermoscopic lesion area is identified by: Obtaining a dermoscopic image and running a convolutional neural network image classifier on the dermoscopic image to obtain pixelwise lesion prediction scores. Segmenting the dermoscopic image into super-pixels, and computing for each super-pixel an average of the pixelwise prediction scores for pixels within that super-pixel. Computing a mean prediction score across the plurality of super-pixels. Assigning a confidence indicator of “1” to each super-pixel with a prediction score equal or greater than the mean prediction score, and a confidence indicator of “0” to each super-pixel with a prediction score less than the mean prediction score.

Abstract: An AMD prediction model utilizes an OCT image estimation model. The OCT image estimation module is created by segmenting an OCT image to generate an OCT projection image for each of multiple biological layers; extracting from each of the generated OCT projection images a first set of features; extracting a second set of features from an input retinal fundus image; for each respective biological layer, registering the input retinal fundus image to the respective OCT projection image by matching at least some of the second set of features with corresponding ones of the first set of features; repeating the above with changes to the input retinal fundus image; and modelling how the changes to the input retinal fundus image are manifest at the correspondingly registered projection images. Estimated OCT projection images can then be generated for the multiple biological layers from a given retinal fundus image.

Abstract: An AMD prediction model utilizes an OCT image estimation model. The OCT image estimation module is created by segmenting an OCT image to generate an OCT projection image for each of multiple biological layers; extracting from each of the generated OCT projection images a first set of features; extracting a second set of features from an input retinal fundus image; for each respective biological layer, registering the input retinal fundus image to the respective OCT projection image by matching at least some of the second set of features with corresponding ones of the first set of features; repeating the above with changes to the input retinal fundus image; and modelling how the changes to the input retinal fundus image are manifest at the correspondingly registered projection images. Estimated OCT projection images can then be generated for the multiple biological layers from a given retinal fundus image.

Abstract: A dermoscopic lesion area is identified by: Obtaining a dermoscopic image and running a convolutional neural network image classifier on the dermoscopic image to obtain pixelwise lesion prediction scores. Segmenting the dermoscopic image into super-pixels, and computing for each super-pixel an average of the pixelwise prediction scores for pixels within that super-pixel. Computing a mean prediction score across the plurality of super-pixels. Assigning a confidence indicator of “1” to each super-pixel with a prediction score equal or greater than the mean prediction score, and a confidence indicator of “0” to each super-pixel with a prediction score less than the mean prediction score.

Abstract: A method for segmenting a target image includes receiving the target image of an anatomical structure, registering a plurality of atlases to the target image, each of the atlases including an image and a plurality of labels corresponding to portions of the image, selecting a plurality of registered atlases, transferring the labels of selected registered atlases to the target image, combining the labels that are transferred to the target image using a fusion of a discriminative model and a generative model, and outputting a segmentation of the target image isolating the anatomical structure, wherein a segmentation of the target image is displayed.

Abstract: Automatically determining image quality of a machine generated image may generate a local saliency map of the image to obtain a set of unsupervised features. The image is run through a trained convolutional neural network (CNN) to extract a set of supervised features from a fully connected layer of the CNN, the image convolved with a set of learned kernels from the CNN to obtain a complementary set of supervised features. The set of unsupervised features and the complementary set of supervised features are combined, and a first decision on gradability of the image is predicted. A second decision on gradability of the image is predicted based on the set of supervised features. Whether the image is gradable is determined based on a weighted combination of the first decision and the second decision.

Abstract: Jointly determining image segmentation and characterization. A computer-generated image of an organ may be received. Organ characteristics estimation may be performed to predict the organ characteristics considering organ segmentation. Organ segmentation may be performed to delineate the organ in the image considering the organ characteristics. A feedback loop feeds the organ characteristics estimation to determine the organ segmentation, and feeds back the organ segmentation to determine the organ characteristics estimation.

Abstract: Automatically determining image quality of a machine generated image may generate a local saliency map of the image to obtain a set of unsupervised features. The image is run through a trained convolutional neural network (CNN) to extract a set of supervised features from a fully connected layer of the CNN, the image convolved with a set of learned kernels from the CNN to obtain a complementary set of supervised features. The set of unsupervised features and the complementary set of supervised features are combined, and a first decision on gradability of the image is predicted. A second decision on gradability of the image is predicted based on the set of supervised features. Whether the image is gradable is determined based on a weighted combination of the first decision and the second decision.

Abstract: A method, system, and computer program product to perform image-based estimation of a risk of a vehicle having a specified status include receiving images from one or more cameras, obtaining one or more vehicle images of the vehicle from the image, classifying the vehicle based on the one or more vehicle images to determine a vehicle classification, extracting features from the one or more vehicle images based on the vehicle classification, and comparing the features with risk indicators to determine estimation of the risk. Instructions are provided for an action based on the risk.

Abstract: A method for segmenting a target image includes receiving the target image of an anatomical structure, registering a plurality of atlases to the target image, each of the atlases including an image and a plurality of labels corresponding to portions of the image, selecting a plurality of registered atlases, transferring the labels of selected registered atlases to the target image, combining the labels that are transferred to the target image using a fusion of a discriminative model and a generative model, and outputting a segmentation of the target image isolating the anatomical structure, wherein a segmentation of the target image is displayed.

Abstract: A method for segmenting a target image includes receiving the target image of an anatomical structure, registering a plurality of atlases to the target image, each of the atlases including an image and a plurality of labels corresponding to portions of the image, selecting a plurality of registered atlases, transferring the labels of selected registered atlases to the target image, combining the labels that are transferred to the target image using a fusion of a discriminative model and a generative model, and outputting a segmentation of the target image isolating the anatomical structure, wherein a segmentation of the target image is displayed.

Abstract: A method for segmenting a target image includes receiving the target image of an anatomical structure, registering a plurality of atlases to the target image, each of the atlases including an image and a plurality of labels corresponding to portions of the image, selecting a plurality of registered atlases, transferring the labels of selected registered atlases to the target image, combining the labels that are transferred to the target image using a fusion of a discriminative model and a generative model, and outputting a segmentation of the target image isolating the anatomical structure, wherein a segmentation of the target image is displayed.